The following report details the methods used to determine appropriate filter thresholds for SNV variant calls.

Creating simulated data

At the site level, three major filters were applied to obtain a high-quality variant set:

• QD - variant quality normalized by read depth
• SOR - strand odds ratio (SOR)
• FS Fisherstrand

To find the optimal filter thresholds, we examined each metric independently and in combination.

Note: filter thresholds were only optimized for SNVs.

• 1. A truth set of variants was created using either a subset of real variants from the 2018 release, or randomly selected positions across the genome. The results and conclusions were essentially the same with both sets of truth variants, so only the former results are shown below.

• 2. The truth set of variants were inserted into the N2 strain bam (N2.bam) using bamsurgeon (20d431e).

• 3. Variants were called with the wi-gatk-nf pipeline.

QD filter threshold

To reduce complexity, the filter thresholds were optimized independently.

The optimal QD threshold were determined as follows:

• 1. For each filter threshold, variants were classified as detected or undetected. For example, the table below illustrates a few rows of the filter threshold QD > 10. Evidence for a variant may be detected, but if the QD or other metric fails a filter it is classified as undetected.

CHROM	POS	QD	sim1_genotype	sim2_genotype	sim3_genotype	==>	pass_QD_filter	is_detected
I	1352	110	1/1	1/1	1/1	QD threshold is 10	yes	yes
I	2566	90	1/1	0/0	1/1	QD threshold is 10	yes	yes
I	3847	2	0/0	1/1	0/0	QD threshold is 10	no	no
I	4975	38	1/1	0/0	0/0	QD threshold is 10	no	no
I	5590	298	1/1	1/1	1/1	QD threshold is 10	yes	yes

• 2. Each variant, depending on whether it is detected and whether it is in the truth set, will fall into 1 of the 4 categories: true positive, true negative, false positive and false negative.

CHROM	POS	is_detected	is_in_truth	category
I	1352	yes	yes	true positive
I	2566	yes	no	false positve
I	3847	no	no	true negative
I	4975	no	yes	false negative
I	5590	yes	yes	true positive

• 3. A confusion matrix can then be created using the variant counts for each category.

	in_truth	not_in_truth
detected	count of true positive	count of false positive
not_detected	count of false negative	count of true negative

• 4. Filter thresholds were chosen to maximize true positive rate and precision, while minimizing false positive rate. Red points indicate the chosen thresholds to use for each filter.

SOR threshold

• The same steps were taken to find an optimal SOR threshold as above.

FS treshold

• The same steps were taken to find an optimal FS threshold.

QD, SOR, FS filter thresholds in combination

• A subset of QD / SOR / FS thresholds were examined in combination. Red dots represent thresholds chosen from independent analysis of each filter, and indicate that thresholds identified by examining filters individually matches closely thresholds arrived at by examining filters in combination.